Towards Automatic Medical Report Classification in Czech

Pavel Pribán, Josef Baloun, Jirí Martínek, Ladislav Lenc, Martin Prantl, Pavel Král

Abstract: This paper deals with the automatic classification of medical reports in the form of unstructured texts in Czech. The outcomes of this work are intended to be integrated into a coding assistant, a system that will help the clinical coders with the manual coding of the diagnoses. To solve this task, we compare several approaches based on deep neural networks. We compare the models in two different scenarios to show their advantages and drawbacks. The results demonstrate that hierarchical GRU with attention outperforms all other models in both cases. The experiments further show that the system can significantly reduce the workload of the operators and thus also saves time and money. To the best of our knowledge, this is the first attempt at automatic medical report classification in the Czech language.